评估错误发现率控制在并联质谱分析中使用陷的评估
Bo Wen1, Jack Freestone2, Michael Riffle1
1Department of Genome Sciences, University of Washington.
bioRxiv : the preprint server for biology
|June 19, 2024
概括
在质谱蛋白质组学软件中,精确的错误控制至关重要,但人们对其了解甚少. 这项研究揭示了当前错误发现率 (FDR) 验证方法的缺陷,特别是数据独立获取 (DIA) 数据,影响蛋白质组学分析的可靠性.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 质谱测量质量谱测量
- 统计验证 统计验证
背景情况:
- 评估质谱蛋白质组学软件的错误控制是一个重大挑战.
- 当前的软件工具采用了多种多样的,经常是未经记录的,不一致的验证错误控制方法.
- 现有的错误发现率 (FDR) 验证策略是不充分的,导致对分析可靠性的理解较差,特别是在数据独立获取 (DIA) 数据方面.
研究的目的:
- 为验证FDR控制,严格描述不同捕获方法的行为.
- 提出一种更强大的方法来评估蛋白质组学软件中的FDR控制.
- 评估流行搜索工具的FDR控制性能,特别是DIA数据.
主要方法:
- 陷实验的理论制定,以严格描述验证方法.
- 开发和应用一种更强大的方法来评估FDR控制.
- 在数据依赖获取 (DDA) 和数据独立获取 (DIA) 设置中对陷分析的实证验证.
- 使用拟议和现有技术,对受欢迎的蛋白质组学搜索工具进行表征.
主要成果:
- 确定了三种常见的FDR验证方法:一种是无效的,一种仅提供下限,一种是有效但功率不足的.
- 评估的DIA搜索工具中没有一个在水平上始终控制FDR.
- 蛋白质组学工具在分析单细胞数据集方面存在重大挑战,特别是关于FDR控制.
结论:
- 目前在蛋白质组学中的FDR验证方法不足,需要改进方法.
- 流行的DIA搜索工具无法始终控制水平的FDR,这引发了对数据可靠性的担忧.
- 进一步开发和验证FDR控制方法对于准确的蛋白质组学数据分析至关重要,特别是对于复杂的数据集,如单细胞样本.
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